From c265c2ec7086cbb0d0c55c0670e7229ef60c335e Mon Sep 17 00:00:00 2001 From: Gillian Minnehan Date: Tue, 27 Aug 2024 11:30:50 -0400 Subject: [PATCH 1/4] publish: memory usage pattern debugging article ### Summary We have a new article about memory usage debugging, and a new author, Bert Schiettecatte! ### Test Plan Visual check --- _data/authors.yml | 7 +- _posts/2024-08-27-memory-debugging.md | 94 ++++++++++++++++++ img/author/bertschiettecatte.png | Bin 0 -> 76516 bytes img/memory-usage-in-audio-system/percussa.png | Bin 0 -> 750103 bytes img/memory-usage-in-audio-system/trace1.png | Bin 0 -> 637791 bytes img/memory-usage-in-audio-system/trace2.png | Bin 0 -> 495119 bytes 6 files changed, 100 insertions(+), 1 deletion(-) create mode 100644 _posts/2024-08-27-memory-debugging.md create mode 100644 img/author/bertschiettecatte.png create mode 100644 img/memory-usage-in-audio-system/percussa.png create mode 100644 img/memory-usage-in-audio-system/trace1.png create mode 100644 img/memory-usage-in-audio-system/trace2.png diff --git a/_data/authors.yml b/_data/authors.yml index 937e0f2a..b9c1e192 100644 --- a/_data/authors.yml +++ b/_data/authors.yml @@ -269,4 +269,9 @@ mschulte: image: /img/author/mschulte.jpg github: schultetwin linkedin: https://www.linkedin.com/in/schulte-mark - blurb: is a firmware engineer trying to firmware \ No newline at end of file + blurb: is a firmware engineer trying to firmware +bertschiettecatte: + name: Bert Schiettecatte + image: /img/author/bertschiettecatte.jpg + linkedin: https://www.linkedin.com/in/bertschiettecatte/ + blurb: is the founder of Noisetron LLC, a software & hardware engineering and expert witness consulting practice, and the founder of Percussa, a pro-audio consumer electronics company. diff --git a/_posts/2024-08-27-memory-debugging.md b/_posts/2024-08-27-memory-debugging.md new file mode 100644 index 00000000..14f3aaf1 --- /dev/null +++ b/_posts/2024-08-27-memory-debugging.md @@ -0,0 +1,94 @@ +--- +title: How Memory Usage Patterns Can Derail Real-time Performance +description: + A story about memory usage causing real-time performance issues in an audio system. +author: bertschiettecatte +--- + + + +In this article, we will learn how memory usage patterns can affect the real-time performance of an embedded application, drawing from a recent experience tracing an audio DSP application running on an embedded Linux platform. First, I will introduce the product in question and the real-time audio software I developed for it. Then, I’ll describe the issues I encountered with audio callbacks and the strategy I followed to determine the cause of the issues, ending with my solution and lessons learned. + + + +Optional motivation to continue onwards + +{% include newsletter.html %} + +{% include toc.html %} + +## Background + +As the founder of Percussa, I have led the software and hardware engineering of our pro-audio consumer electronics products for nearly 20 years. The first product I developed was the [AudioCubes](https://www.percussa.com/what-are-audiocubes/), a human-computer interface, consisting of multiple smart wireless objects capable of detecting each other’s location, orientation, and distance. The AudioCubes are a bare metal DSP platform and do not run an off-the-shelf operating system. + +Subsequently, I developed the [Percussa Super Signal Processor (SSP)](https://www.percussa.com/super-signal-processor/), a multichannel audio DSP platform, which was funded via Kickstarter for 314%. The SSP is used by professional studio musicians and sound designers world-wide and is a eurorack module (eurorack is a modular synthesizer standard). It is installed into end users’ synthesizer racks or in recording studios. It offers a library of DSP building blocks which can be chained together to create various signal processing chains, for synthesizing new sounds or processing existing sounds. + +The SSP is a Linux-based platform, based on a Rockchip SoC. In addition to developing the hardware and bringing up the platform, I also developed the real-time audio DSP software for the SSP. To deliver a great customer experience, it was important to get consistent audio DSP performance from the platform, avoiding the clicks and pops in the audio that can be caused by not consistently meeting audio processing deadlines (i.e. completing audio callbacks in time). Additionally, to fully benefit from the processing power of the platform, DSP processing needs to be parallelized over multiple CPU cores (the SoC in the SSP is a quad-core processor). + +

+ Image of Percussa device +

+ +The audio DSP software I developed originally ran on Windows and Mac OSX platforms using a popular C++ application framework for audio applications. My strategy was to port the software to the new audio-embedded Linux platform (the SSP). + +This went fairly well, with most of my time spent designing and implementing user interface code, menu systems, and code to parallelize DSP code across CPU cores. My goal on my embedded Linux platform was to use the mainline kernel tree to reduce maintenance costs. + +## The problem + +As I started testing and debugging my application, I kept running into issues where the audio output was regularly interrupted, resulting in an audible click. What could possibly be wrong with my code that might cause this? I started thinking about the reasons my audio callback could take too long to return, the locks in my code, and whether the audio callback could be pre-empted by the operating system, causing it to return too late and miss its real-time “deadline.” + +Eventually I decided to use a profiler to figure out what was going on and turned to the excellent [Tracy profiler](https://github.com/wolfpld/tracy), which is open source and free. Tracy is easy to integrate with your code, allowing you to define different zones for tracing and profiling. The Tracy profiling GUI application can connect over the network with your embedded platform and receive trace information in real-time. This helps speed up development and debugging. Tracy also comes with a command-line capture tool you can use to capture the trace directly to disk instead of sending it over the network to your workstation. + +I typically do not enable Tracy in release builds shipped to customers and only use it when I’m trying to find a problem with the real-time behavior of my software. I use compiler switches (`-D`) to enable Tracy, as described in the Tracy manual, which also describes how to control the amount of data collected. This is important, as you do not want to generate so much tracing data that it starts to get in the way of determining the real problem at hand. + +

+ Image of Tracy (trace 1) +

+ +With the help of Tracy, I discovered that my audio callback was occasionally taking too long to return. This happened often enough that it was a real problem. + +In the above screenshot, you can see an instance when the ALSA callback takes much longer than normal. At the bottom of the screen, you can see the different nested zones that I defined in my source code, with Tracy measuring the time spent in each. The zones allow you to understand which areas of your code are the most problematic and need review. + +What was interesting was that the issue always seemed to be connected to a memory-related function call. Right above the zones at the bottom, you can see a series of blue dots. When hovering over them, these dots showed the same memory-related function repeatedly, indicating that the CPU was spending an unusually long time in this function. Additionally the yellow graph at the bottom (indicated by yellow arrows) shows memory usage constantly increasing or decreasing, resulting in the zig-zag pattern being plotted. + +However, I had paid close attention to the code in my audio callback and DSP worker threads, and I was fairly confident I was not doing any kind of dynamic memory allocation in my audio or DSP threads. + +As I dug deeper, I noticed that my audio thread was being pre-empted. ALSA support was being provided by the third-party framework. Eventually I started examining the memory usage behavior of my application (and third-party framework) and discovered that the application framework I was using had its own memory management classes. Under the hood, `malloc()` was being called to allocate blocks of very small sizes—some as small as 4 bytes! These classes were used all over the code base in the framework—and thus in my application, which was based on the framework. I had unwittingly invited dynamic memory allocation in places due to the way the framework was architected. + +Because of the frequency with which these calls happened, it resulted in memory fragmentation, which explained why some memory-related function calls took so long to return. Fragmentation can result in memory-related function calls taking much longer than normal and/or the Linux kernel pre-empting your thread(s) while it performs its cleanup. + +Tracy supports sampling call stacks, and it can also sample Linux kernel call stacks (refer to the Tracy manual to understand the requirements for this). + +

+ Image of Tracy (trace 2) +

+ +At the top of the screen in Tracy, you can see which CPU cores the threads are being scheduled on (note the four “lanes” in the above screenshot, since the software is running on a quad-core CPU). + +Tracy can also show when kernel threads are being scheduled, as shown in grey in the screenshot above, which can help you understand if any of your threads are being pre-empted. You can see that the ALSA thread is being pre-empted, with a memory-related function call appearing at the bottom of the screen shortly before this happens. + +By analyzing the call stacks, combined with callbacks taking unusually long to return, excessive time spent in memory-related functions, and the scheduling of kernel threads, you can get an idea of whether your application’s memory usage patterns might be causing issues. + +In my particular case, my application’s memory usage patterns caused my callbacks and other code to take too long to return, resulting in missed real-time deadlines and audio clicks. + +## The solution + +To solve this problem, I implemented my own memory allocator and re-implemented `malloc()` and friends to use my allocator. At a high level, my allocator pre-allocates one large block of memory using `mmap()` on startup and manages free/used lists of smaller blocks. Calls to `malloc()` and friends then result in allocations/deallocations using my allocator. This approach addresses the above fragmentation issues and prevents memory-related function calls from taking too long or varying unpredictably. + +I considered using off-the-shelf allocators, but this experience made me a bit paranoid. I decided that implementing one myself was a good exercise anyway so I could be aware of its behavior under the hood. By implementing my own allocator and overriding `malloc()` and friends to use my allocator, I was able to collect and display allocation statistics and understand how my application uses memory as I was debugging and testing it. + +I also reviewed my code and re-architected parts of it to pre-allocate pools of objects on startup, which are then re-used as much as possible. + +While continuing my efforts with profiling and tracing using Tracy, I also learned the importance of being aware of the memory allocation behavior of the tracing code (and third-party framework code!) that you integrate in your application. Tracy has its own allocator, which is great, but one needs to be mindful of the tracing features enabled and the volume of data being collected to avoid affecting the real-time behavior of the application being traced. + +## Conclusion + +If you are working on a real-time application—whether it is an audio processing application, a low-latency proprietary trading application, or anything else that needs to have real-time or low-latency behavior—profile your application in various ways and be aware of the memory usage patterns of your application. And don't forget to examine the behavior under the hood of ALL third-party libraries in your application (including tracing or profiling code or libraries). 🤓 + + +{% include newsletter.html %} + +{% include submit-pr.html %} + + +{:.no_toc} diff --git a/img/author/bertschiettecatte.png b/img/author/bertschiettecatte.png new file mode 100644 index 0000000000000000000000000000000000000000..98c0f22d58d6c86436b3f82b33aa646ebcb58846 GIT binary patch literal 76516 zcmZ^~2{>C_v@lF3Z7D4+`WjnB)sU*zEa}`TlD68?*Gx%Oh?s{Yw2G#twkTdh+ES^S ziwVb@sUS%2|7zwbxmD9n5|3onuG;Iw~L_ zaLn}X&4&U4g2(@b!-w`-f{v=6+N-#@-?(w#^u`Uv`#x|tcbKby!1?zsj*c^?8t2=c zoE#n7d(_pA`UF0F^CtG8V^~vTlOm~+*w~?%m1=7{C4GEq|D$Gs><3Mi9wM=d+#g!) z1y#x$)dqzs>OKch;HOEQCBl{_N2l(YYJv~-hlP)xtN!%u^~;yP1lmQ<1JndcWx`?a z&wk1dr>iLj7{0FG*8+Z3vK2n``hdZHvQ#+isbNyTl!cZG?s&t=88^!BJ(MAZn+ zg=7uZ{d82+6l}R9q9sx!0=)XI@p!jmi=$}EZIPEEz51J};Y@vpp?@==FNo5e&#Boo7*Tv2Lq zc9E9G3-U?6B4=_Z4{m}L_7mT$9>mU{W_X!9@y9*rnuQA}g{NMC$5B|mZ&wT%fC;_3pn)n_BecJbb 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